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Kinome‐Wide Profiling Prediction of Small Molecules
Author(s) -
Sorgenfrei Frieda A.,
Fulle Simone,
Merget Benjamin
Publication year - 2018
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201700180
Subject(s) - kinome , kinase , computational biology , profiling (computer programming) , drug discovery , predictive power , small molecule , mutant , computer science , biology , bioinformatics , biochemistry , physics , quantum mechanics , gene , operating system
Extensive kinase profiling data, covering more than half of the human kinome, are available nowadays and allow the construction of activity prediction models of high practical utility. Proteochemometric (PCM) approaches use compound and protein descriptors, which enables the extrapolation of bioactivity values to thus far unexplored kinases. In this study, the potential of PCM to make large‐scale predictions on the entire kinome is explored, considering the applicability on novel compounds and kinases, including clinically relevant mutants. A rigorous validation indicates high predictive power on left‐out kinases and superiority over individual kinase QSAR models for new compounds. Furthermore, external validation on clinically relevant mutant kinases reveals an excellent predictive power for mutations spread across the ATP binding site.